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  <div class="headertitle"><div class="title">NonMarkovPole.h</div></div>
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<a href="_non_markov_pole_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * \brief       Objective function for single and double poles with partial state information (non-Markovian task) </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * </span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * Class for balancing one or two poles on a cart using a fitness</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * function that decreases the longer the pole(s) balance(s).  Based</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * on code written by Verena Heidrich-Meisner for the paper</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * </span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * V. Heidrich-Meisner and C. Igel. Neuroevolution strategies for</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * episodic reinforcement learning. Journal of Algorithms,</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * 64(4):152–168, 2009.</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \author      Johan Valentin Damgaard</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> *</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> *</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * </span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> * </span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="comment"> *</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="comment"> */</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#ifndef SHARK_OBJECTIVEFUNCTIONS_BENCHMARKS_POLE_NONMARKOV_OBJECTIVE_FUNCTION</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARKS_POLE_NONMARKOV_OBJECTIVE_FUNCTION</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;exception&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_objective_function_8h.html" title="AbstractObjectiveFunction.">shark/ObjectiveFunctions/AbstractObjectiveFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;shark/Models/OnlineRNNet.h&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="preprocessor">#include &lt;<a class="code" href="_base_8h.html">shark/LinAlg/Base.h</a>&gt;</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span> </div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="preprocessor">#include &lt;<a class="code" href="_single_pole_8h.html">shark/ObjectiveFunctions/Benchmarks/PoleSimulators/SinglePole.h</a>&gt;</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="preprocessor">#include &lt;<a class="code" href="_double_pole_8h.html">shark/ObjectiveFunctions/Benchmarks/PoleSimulators/DoublePole.h</a>&gt;</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span> </div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {<span class="keyword">namespace </span>benchmarks{</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment"></span> </div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// \brief Objective function for single and double non-Markov poles</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// </span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// Class for balancing one or two poles on a cart using a fitness function</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// that decreases the longer the pole(s) balance(s).</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// Based on code written by Verena Heidrich-Meisner for the paper</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// </span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// V. Heidrich-Meisner and C. Igel. Neuroevolution strategies for episodic reinforcement learn-ing. Journal of Algorithms, 64(4):152–168, 2009.</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// \ingroup benchmarks</span></div>
<div class="foldopen" id="foldopen00060" data-start="{" data-end="};">
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html">   60</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1benchmarks_1_1_non_markov_pole.html" title="Objective function for single and double non-Markov poles.">NonMarkovPole</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a> {</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span> </div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">  /// \param single Is this an instance of the single pole problem?</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">  /// \param hidden Number of hidden neurons in underlying neural network</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">  /// \param bias Whether to use bias in neural network</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">  /// \param sigmoidType Activation sigmoid function for neural network</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">  /// \param normalize Whether to normalize input before use in neural network</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">  /// \param max_pole_evaluations Balance goal of the function, i.e. number of steps that pole should be able to balance without failure</span></div>
<div class="foldopen" id="foldopen00069" data-start="{" data-end="}">
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a83d4df6fff72ddb0395e762474f69942">   69</a></span><span class="comment"></span>  <a class="code hl_function" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a83d4df6fff72ddb0395e762474f69942">NonMarkovPole</a>(<span class="keywordtype">bool</span> single, std::size_t hidden, <span class="keywordtype">bool</span> bias, </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        RecurrentStructure::SigmoidType sigmoidType = RecurrentStructure::FastSigmoid,</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        <span class="keywordtype">bool</span> normalize = <span class="keyword">true</span>,</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        std::size_t max_pole_evaluations = 100000)</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    : m_single(single),</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>      m_maxPoleEvals(max_pole_evaluations),</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>      m_normalize(normalize) {</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    <span class="keywordflow">if</span> (sigmoidType == RecurrentStructure::Linear) {</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>      std::cerr &lt;&lt; <span class="stringliteral">&quot;Cannot use linear activation function for pole balancing.&quot;</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>       &lt;&lt; std::endl;</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>      exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    }</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span> </div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    <span class="comment">// number of inputs should be 2 for single pole, 3 for double.</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    std::size_t inputs = 0;</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    <span class="keywordflow">if</span> (single) {</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>      inputs = 2;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    }</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>      inputs = 3;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    }</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>    <span class="comment">// set features</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aab9262b57bb302f04b2561666a9068446" title="The function can propose a sensible starting point to search algorithms.">CAN_PROPOSE_STARTING_POINT</a>;</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    </div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="comment">// set number of variables/weights.</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    <span class="comment">// number of outputs is always 1. </span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    <span class="comment">// dimensions depend on whether we use bias</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    <span class="keywordflow">if</span> (bias){</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>      m_dimensions = (hidden + 1) * (hidden + 1) +</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    inputs * (hidden + 1) + hidden + 1;</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>    }</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>    <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>      m_dimensions = (hidden + 1) * (hidden + 1) + inputs * (hidden + 1);</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }    </div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    </div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    <span class="comment">// make RNNet</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    mp_struct = <span class="keyword">new</span> RecurrentStructure();</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>    mp_struct-&gt;setStructure(inputs, hidden, 1, bias, sigmoidType);</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    mp_net = <span class="keyword">new</span> PoleRNNet(mp_struct);</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    </div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>    <span class="comment">// check dimensions match</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    <span class="keywordflow">if</span>(m_dimensions != mp_net-&gt;numberOfParameters()) {</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>      std::cerr &lt;&lt; <span class="stringliteral">&quot;Non-Markov pole RNNet: Dimensions do not match, &quot;</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>       &lt;&lt; m_dimensions &lt;&lt; <span class="stringliteral">&quot; != &quot;</span> &lt;&lt;  mp_net-&gt;numberOfParameters() &lt;&lt; std::endl;</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>      exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>    }</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>    </div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>    <span class="comment">// set eval count</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#af0942c072be06d0dd4da5ee7067c5777" title="Evaluation counter, default value: 0.">m_evaluationCounter</a> = 0;</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>  }</div>
</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span> </div>
<div class="foldopen" id="foldopen00121" data-start="{" data-end="}">
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#ae6777acea00793e7a9cd12c4c114f023">  121</a></span>  <a class="code hl_function" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#ae6777acea00793e7a9cd12c4c114f023">~NonMarkovPole</a>(){</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    <span class="keyword">delete</span> mp_struct;</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    <span class="keyword">delete</span> mp_net;</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>  }</div>
</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>  </div>
<div class="foldopen" id="foldopen00126" data-start="{" data-end="}">
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#aad8c006d1988501c681401955d52ceac">  126</a></span>  std::string <a class="code hl_function" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#aad8c006d1988501c681401955d52ceac">name</a>() {</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    <span class="keywordflow">return</span> <span class="stringliteral">&quot;Objective Function for Non-Markovian pole balancing.&quot;</span>;</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>  }</div>
</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>  <span class="comment"></span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">  /// \brief Returns degrees of freedom</span></div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a56b23bc7588d0354b73b394188e9a872">  131</a></span><span class="comment"></span>  std::size_t <a class="code hl_function" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a56b23bc7588d0354b73b394188e9a872" title="Returns degrees of freedom.">numberOfVariables</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    <span class="keywordflow">return</span> m_dimensions;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>  }</div>
</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>  <span class="comment"></span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">  /// \brief Always proposes to start in a zero vector with appropriate degrees of freedom</span></div>
<div class="foldopen" id="foldopen00136" data-start="{" data-end="}">
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a1476a34b5e8086c8e5d2a1eea5270d59">  136</a></span><span class="comment"></span>  <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <a class="code hl_function" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a1476a34b5e8086c8e5d2a1eea5270d59" title="Always proposes to start in a zero vector with appropriate degrees of freedom.">proposeStartingPoint</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>    <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> startingPoint(m_dimensions);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    <span class="keywordflow">for</span>(std::size_t i = 0; i != m_dimensions; i++) {</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>      startingPoint(i) = 0.0;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    }</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    <span class="keywordflow">return</span> startingPoint;</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>  }</div>
</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>  <span class="comment"></span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment">  /// \brief Evaluates weight vector on fitness function</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment">  /// \param input Vector to be evaluated.</span></div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">  /// \return Fitness of vector</span></div>
<div class="foldopen" id="foldopen00147" data-start="{" data-end="}">
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"><a class="line" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a6b27effc1ffb168e031c5c67756406c1">  147</a></span><span class="comment"></span>  <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a70f0672a3c3b24c437c81243624b5307">ResultType</a> <a class="code hl_function" href="classshark_1_1benchmarks_1_1_non_markov_pole.html#a6b27effc1ffb168e031c5c67756406c1" title="Evaluates weight vector on fitness function.">eval</a>(<span class="keyword">const</span> <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> &amp;input)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>    <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(input.size() == m_dimensions);</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#af0942c072be06d0dd4da5ee7067c5777" title="Evaluation counter, default value: 0.">m_evaluationCounter</a>++;</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    </div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>    <span class="keywordflow">if</span>(m_single) {</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>      <span class="keywordflow">return</span> evalSingle(input);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>      <span class="keywordflow">return</span> evalDouble(input);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>    }</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>  }</div>
</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>  </div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>  <span class="comment">// private class for recurrent neural network. not be used outside main class.</span></div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>  <span class="keyword">class </span>PoleRNNet : <span class="keyword">public</span> OnlineRNNet {</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>  <span class="keyword">public</span>:</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>    PoleRNNet(RecurrentStructure* structure) : OnlineRNNet(structure){}</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    boost::shared_ptr&lt;State&gt; createState()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;State not available for PoleRNNet.&quot;</span>);</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    }</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    <span class="keywordtype">void</span> eval(BatchInputType <span class="keyword">const</span> &amp; patterns, BatchOutputType &amp;outputs,</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>          State&amp; state)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        <span class="keywordflow">throw</span> std::logic_error(<span class="stringliteral">&quot;Batch not available for PoleRNNet.&quot;</span>);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>    }</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>  };</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment"></span> </div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">  /// \brief Converts neural network output for use with pole simulator</span></div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">  /// \param output Output of the neural network.</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">  /// \return double precision floating point between 0 and 1.</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment"></span>  <span class="keywordtype">double</span> convertToPoleMovement(<span class="keywordtype">double</span> output)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>    <span class="keywordflow">switch</span>(mp_struct-&gt;sigmoidType())</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>      {</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>      <span class="keywordflow">case</span> RecurrentStructure::Logistic:</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>    <span class="keywordflow">return</span> output;</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>      <span class="keywordflow">case</span> RecurrentStructure::FastSigmoid:</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    <span class="keywordflow">return</span> (output + 1.) / 2.;</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>      <span class="keywordflow">case</span> RecurrentStructure::Tanh:</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>    <span class="keywordflow">return</span> (output + 1.) / 2.;</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>      <span class="keywordflow">default</span>:</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    std::cerr &lt;&lt; <span class="stringliteral">&quot;Unsupported activation function for pole balancing.&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    exit(EXIT_FAILURE);</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>      }</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>  }</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>  <span class="comment"></span></div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span><span class="comment">  /// \brief Fitness function for single poles. Gets lower as pole balances for longer.</span></div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span><span class="comment">  /// \param input Vector to be evaluated.</span></div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="comment">  /// \return Fitness of vector</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="comment"></span>  <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a70f0672a3c3b24c437c81243624b5307">ResultType</a> evalSingle(<span class="keyword">const</span> <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> &amp;input)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>    <span class="keywordtype">double</span> init_angle = 0.07;</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>    SinglePole pole(<span class="keyword">false</span>, m_normalize);</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    RealVector state(2);</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>    RealMatrix output(1,1); </div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>    RealMatrix inState(1,2);</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>    std::size_t eval_count = 0;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    <span class="keywordtype">bool</span> failed = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>    </div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    pole.init(init_angle);</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>    mp_net-&gt;resetInternalState();</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>    mp_net-&gt;setParameterVector(input);</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>    </div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>    <span class="keywordflow">while</span>(!failed &amp;&amp; eval_count &lt; m_maxPoleEvals) {</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>      pole.getState(state);</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>      row(inState,0) = state;</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>      mp_net-&gt;eval(inState,output);</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>      pole.move(convertToPoleMovement(output(0,0)));</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>      failed = pole.failure();</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>      eval_count++;</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>    }</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>    </div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>    <span class="comment">// gets lower as number of evaluations grows. min = 0</span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>    <span class="keywordflow">return</span> m_maxPoleEvals - eval_count;</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>  }</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span><span class="comment"></span> </div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment">  /// \brief Fitness function for double poles. Gets lower as poles balance for longer.</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment">  /// \param input Vector to be evaluated.</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="comment">  /// \return Fitness of vector</span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment"></span>  <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a70f0672a3c3b24c437c81243624b5307">ResultType</a> evalDouble(<span class="keyword">const</span> <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> &amp;input)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>    <span class="keywordtype">double</span> init_angle = 0.07;</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>    DoublePole pole(<span class="keyword">false</span>, m_normalize);</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>    RealVector state(3);</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>    RealMatrix output(1,1); </div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>    RealMatrix inState(1,3);</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>    std::size_t eval_count = 0;</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>    <span class="keywordtype">bool</span> failed = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>    </div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>    pole.init(init_angle);</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>    mp_net-&gt;resetInternalState();</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>    mp_net-&gt;setParameterVector(input);</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    </div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>    <span class="keywordflow">while</span>(!failed &amp;&amp; eval_count &lt; m_maxPoleEvals) {</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>      pole.getState(state);</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>      row(inState,0) = state;</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>      mp_net-&gt;eval(inState,output);</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>      pole.move(convertToPoleMovement(output(0,0)));</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>      failed = pole.failure();</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>      eval_count++;</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>    }</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>    <span class="comment">// gets lower as number of evaluations grows. min = 0</span></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>    <span class="keywordflow">return</span> m_maxPoleEvals - eval_count;</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>  }</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span><span class="comment"></span> </div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span><span class="comment">  /// True if this is a single pole, false if double pole.</span></div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span><span class="comment"></span>  <span class="keywordtype">bool</span> m_single;<span class="comment"></span></div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span><span class="comment">  /// True if neural network input is normalized, false otherwise</span></div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span><span class="comment"></span>  <span class="keywordtype">bool</span> m_normalize;<span class="comment"></span></div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span><span class="comment">  /// Degrees of freedom</span></div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span><span class="comment"></span>  std::size_t m_dimensions;<span class="comment"></span></div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span><span class="comment">  /// Balance goal</span></div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="comment"></span>  std::size_t m_maxPoleEvals;</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>  <span class="comment"></span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span><span class="comment">  /// Neural network</span></div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span><span class="comment"></span>  RecurrentStructure *mp_struct;</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>  OnlineRNNet *mp_net;</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>  </div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>};</div>
</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span> </div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>}}</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span><span class="preprocessor">#endif</span></div>
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